Emily Judith Arteaga Garcia
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Emily Judith Arteaga Garcia has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Anita Sarma
Oregon State University, Corvallis, OR, USA
1 shared publication - Igor Steinmacher
Northern Arizona University
1 shared publication - João Felipe Pimentel
Universidade Federal Fluminense, Brazil
1 shared publication - Marco Aurélio Gerosa
Northern Arizona University
1 shared publication - Zixuan Feng
Oregon State University, Corvallis, Oregon, USA
1 shared publication
Latest Publications
- Open Access
- 6 February 2024
- Published by Association for Computing Machinery (ACM)
- p. 1-12
How to Support ML End-User Programmers through a Conversational Agent
Machine Learning (ML) is increasingly gaining significance for enduser programmer (EUP) applications. However, machine learning end-user programmers (ML-EUPs) without the right background face a daunting learning curve and a heightened risk of mistakes and flaws in their models. In this work, we designed a conversational agent named "Newton" as an expert to support ML-EUPs. Newton's design was shaped by a comprehensive review of existing literature, from which we identified six primary challenges faced by ML-EUPs and five strategies to assist them. To evaluate the efficacy of Newton's design, we conducted a Wizard of Oz within-subjects study with 12 ML-EUPs. Our findings indicate that Newton effectively assisted ML-EUPs, addressing the challenges highlighted in the literature. We also proposed six design guidelines for future conversational agents, which can help other EUP applications and software engineering activities.
- 6 February 2024
- Published by Association for Computing Machinery (ACM)
- p. 1-12
Open AccessHow to Support ML End-User Programmers through a Conversational Agent
Machine Learning (ML) is increasingly gaining significance for enduser programmer (EUP) applications. However, machine learning end-user programmers (ML-EUPs) without the right background face a daunting learning curve and a heightened risk of mistakes and flaws in their models. In this work, we designed a conversational agent named "Newton" as an expert to support ML-EUPs. Newton's design was shaped by a comprehensive review of existing literature, from which we identified six primary challenges faced by ML-EUPs and five strategies to assist them. To evaluate the efficacy of Newton's design, we conducted a Wizard of Oz within-subjects study with 12 ML-EUPs. Our findings indicate that Newton effectively assisted ML-EUPs, addressing the challenges highlighted in the literature. We also proposed six design guidelines for future conversational agents, which can help other EUP applications and software engineering activities.